The use of group testing in prevalence estimation and disease detection is pervasive because of its time and cost savings. In such settings, human subjects are tested in groups, instead of individually, by pooling individual samples (e.g. blood samples) to determine the presence or absence of a disease. Examples where group testing currently is used include disease prevalence estimation in least developed countries, chlamydia and gonorrhea testing at public health clinics, and blood bank screening worldwide. Until recently, all biomedical applications using group testing have treated human subjects as being sampled from one homogenous population. Treating populations as heterogenous by incorporating covariates through modeling is new. Our "informative group testing" research amplifies the original benefits from group testing by more efficiently detecting which individuals are infected and estimating the prevalence among specific parts of a population. This research involves developing new biostatistical methodology for modeling and for identifying the outcomes of unobserved correlated binary random variables. The specific aims of this research are to (1) develop new methods for disease detection of a single disease through the use of group testing regression models, (2) formulate new modeling and estimation procedures to simultaneously model multiple disease probabilities, (3) create new multiple disease identification procedures using the multiple disease probability models, and (4) investigate biomedical data from a variety of settings to test our statistical procedures. Our informative group testing research will provide methods to more efficiently detect disease and estimate its prevalence. The public health community will benefit from these new methods through quicker and less costly disease detection and understanding which factors are associated with multiple diseases.